code: def cspline1d(signal, lamb=0.0):
      """
      Compute cubic spline coefficients for rank-1 array.
  
      Find the cubic spline coefficients for a 1-D signal assuming
      mirror-symmetric boundary conditions. To obtain the signal back from the
      spline representation mirror-symmetric-convolve these coefficients with a
      length 3 FIR window [1.0, 4.0, 1.0]/ 6.0 .
  
      Parameters
      ----------
      signal : ndarray
          A rank-1 array representing samples of a signal.
      lamb : float, optional
          Smoothing coefficient, default is 0.0.
  
      Returns
      -------
      c : ndarray
          Cubic spline coefficients.
  
      See Also
      --------
      cspline1d_eval : Evaluate a cubic spline at the new set of points.
  
      Examples
      --------
      We can filter a signal to reduce and smooth out high-frequency noise with
      a cubic spline:
  
      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
      >>> from scipy.signal import cspline1d, cspline1d_eval
      >>> rng = np.random.default_rng()
      >>> sig = np.repeat([0., 1., 0.], 100)
      >>> sig += rng.standard_normal(len(sig))*0.05  # add noise
      >>> time = np.linspace(0, len(sig))
      >>> filtered = cspline1d_eval(cspline1d(sig), time)
      >>> plt.plot(sig, label="signal")
      >>> plt.plot(time, filtered, label="filtered")
      >>> plt.legend()
      >>> plt.show()
  
      """
      if lamb != 0.0:
          return _cubic_smooth_coeff(signal, lamb)
      else:
          return _cubic_coeff(signal)
  
file_path: scipy/scipy/signal/_bsplines.py
imports: from numpy import asarray, pi, zeros_like, array, arctan2, tan, ones, arange, floor, r_, atleast_1d, sqrt, exp, greater, cos, add, sin
  from ._spline import cspline2d, sepfir2d
  from ._signaltools import lfilter, sosfilt, lfiltic
  from scipy.interpolate import BSpline
label_desc: Use image node_id 11 for calling a global function with example usage: cspline1d(signal, lamb) and returns: _cubic_smooth_coeff, _cubic_coeff